A review of High Performance Computing foundations for scientists

The increase of existing computational capabilities has made simulation emerge as a third discipline of Science, lying midway between experimental and purely theoretical branches [1, 2]. Simulation enables the evaluation of quantities which otherwise would not be accessible, helps to improve experiments and provides new insights on systems which are analysed [3-6]. Knowing the fundamentals of computation can be very useful for scientists, for it can help them to improve the performance of their theoretical models and simulations. This review includes some technical essentials that can be useful to this end, and it is devised as a complement for researchers whose education is focused on scientific issues and not on technological respects. In this document we attempt to discuss the fundamentals of High Performance Computing (HPC) [7] in a way which is easy to understand without much previous background. We sketch the way standard computers and supercomputers work, as well as discuss distributed computing and discuss essential aspects to take into account when running scientific calculations in computers.Comment: 33 page

Importance of Explicit Vectorization for CPU and GPU Software Performance

Much of the current focus in high-performance computing is on multi-threading, multi-computing, and graphics processing unit (GPU) computing. However, vectorization and non-parallel optimization techniques, which can often be employed additionally, are less frequently discussed. In this paper, we present an analysis of several optimizations done on both central processing unit (CPU) and GPU implementations of a particular computationally intensive Metropolis Monte Carlo algorithm. Explicit vectorization on the CPU and the equivalent, explicit memory coalescing, on the GPU are found to be critical to achieving good performance of this algorithm in both environments. The fully-optimized CPU version achieves a 9x to 12x speedup over the original CPU version, in addition to speedup from multi-threading. This is 2x faster than the fully-optimized GPU version.Comment: 17 pages, 17 figure

CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

Task-based programming for Seismic Imaging: Preliminary Results

International audienceThe level of hardware complexity of current supercomputers is forcing the High Performance Computing (HPC) community to reconsider parallel programming paradigms and standards. The high-level of hardware abstraction provided by task-based paradigms make them excellent candidates for writing portable codes that can consistently deliver high performance across a wide range of platforms. While this paradigm has proved efficient for achieving such goals for dense and sparse linear solvers, it is yet to be demonstrated that industrial parallel codes relying on the classical Message Passing Interface (MPI) standard and that accumulate dozens of years of expertise (and countless lines of code) may be revisited to turn them into efficient task-based programs. In this paper, we study the applicability of task-based programming in the case of a Reverse Time Migration (RTM) application for Seismic Imaging. The initial MPI-based application is turned into a task-based code executed on top of the PaRSEC runtime system. Preliminary results show that the approach is competitive with (and even potentially superior to) the original MPI code on an homogenous multicore node and can exploit much more efficiently complex hardware such as a cache coherent Non Uniform Memory Access (ccNUMA) node or an Intel Xeon Phi accelerator